"Finding the Best Tool for Memory Context Across Agentic Sessions Using Augment Code" matching MCP servers:

Enables AI coding assistants to automatically scan, store, and query API endpoints from codebases, providing instant lookup and semantic search to reduce context switching and token consumption.

An MCP server that provides semantic search over local git repositories, enabling users to clone repositories, process branches, and search code through vectorized code chunks.

Multi-provider embedding MCP server with local Milvus for semantic code search. Works with Claude Code, Cursor, Windsurf, and other MCP-compatible IDEs.

Enables AI agents to maintain long-term, cross-session memory by extracting facts, reconciling state conflicts, and retrieving relevant memories via vector search.

Provides persistent memory for AI agents using hybrid search (vector embeddings + BM25) with neural reranking, enabling storage and retrieval of insights, debugging solutions, and patterns across coding sessions.

Provides persistent context management for AI agents by storing and querying semantic information using Upstash Vector DB and Google AI embeddings. It enables semantic search, batch operations, and metadata filtering to help agents retrieve relevant stored knowledge.

Provides persistent, semantic memory storage for LLMs across sessions using vector embeddings and FAISS search. Features bio-inspired memory consolidation, intelligent forgetting, and semantic retrieval without API costs.

Local-first MCP server for long-term memory of dev sessions. It indexes session metadata with embeddings in Postgres/pgvector, enables semantic search over past sessions, and resolves only relevant session files for context.

MCP Memory is a MCP Server that gives clients like Cursor and Claude the ability to remember user preferences and behaviors across conversations using vector search.

MCP Memory is an MCP Server that gives MCP Clients the ability to remember information about users across conversations. It uses vector search technology to find relevant memories based on meaning, not just keywords.

An MCP server that gives AI assistants the ability to remember user information (preferences, behaviors) across conversations using vector search technology.

Claude Context is an MCP plugin that adds semantic code search to Claude Code and other AI coding agents, giving them deep context from your entire codebase.

Enables AI assistants to remember user information and preferences across conversations using vector search technology. Built on Cloudflare infrastructure with isolated user namespaces for secure, persistent memory storage.

Enables MCP clients to remember user preferences and behaviors across conversations using vector search technology.

Enables Claude to index and retrieve context from codebases using self-hosted Milvus for semantic search, with hardened reliability and security for production use.

Context Portal (ConPort): A memory bank MCP server building a project-specific knowledge graph to supercharge AI assistants. Enables powerful Retrieval Augmented Generation (RAG) for context-aware development in your IDE.

Provides a shared local memory MCP server so multiple coding agents can store, retrieve, and attribute memories across sessions. It enables agents to recall project knowledge and past fixes with provenance.

Enables personal memory management through tools to find people, get profiles, remember notes, and perform semantic search, integrated with Claude via SSE.

An MCP server that gives AI assistants persistent memory across conversations, storing and retrieving user preferences and behaviors using vector search.

Provides persistent semantic memory for Claude Code and Claude Desktop with fully offline local embeddings and HNSW indexing, enabling memory storage, semantic search, listing, and deletion across sessions.